{
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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Agenda\n",
      "\n",
      "- Define the problem and the approach\n",
      "- Data basics: loading data, looking at your data, basic commands\n",
      "- Handling missing values\n",
      "- <p style=\"color: red\">Intro to scikit-learn</p>\n",
      "- Grouping and aggregating data\n",
      "- Feature selection\n",
      "- Fitting and evaluating a model\n",
      "- Deploying your work"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##In this notebook you will\n",
      "\n",
      "- Take a tour of `scikit-learn` and learn what it's used for\n",
      "- Build a toy classifier\n",
      "- Make and vizualize a decision tree\n",
      "- Build your own regression model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##[scikit-learn](http://scikit-learn.org/stable/)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Consistent APIs\n",
      "Algorithms are implemented with the same core functions:\n",
      "\n",
      "- fit = train an algorithm\n",
      "- predict = predict the value for a given record\n",
      "- predict_proba = predict the probability of all possible classes for a given record (classification only)\n",
      "- transform = alter your data based on a given preprocessor (i.e. normalize or scale your data) (preprocessing/unsuperivsed)\n",
      "- fit_transform = train a preprocessor and then transform the data in a single step (preprocessing/unsuperivsed)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<blockquote class=\"twitter-tweet\"><p>Scikit-Learn reply to today&#39;s <a href=\"https://twitter.com/wiseio\">@wiseio</a> Random Forest benchmark: <a href=\"https://t.co/El5at9KvHS\">https://t.co/El5at9KvHS</a> \u2026 Coming soon in the next 0.14 stable release!</p>&mdash; Gilles Louppe (@glouppe) <a href=\"https://twitter.com/glouppe/statuses/357250042538622976\">July 16, 2013</a></blockquote>\n",
      "<script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "\n",
      "iris = load_iris()\n",
      "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
      "df['species'] = iris.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVC\n",
      "from sklearn.neighbors import KNeighborsClassifier"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svm_clf = SVC()\n",
      "neighbors_clf = KNeighborsClassifier()\n",
      "clfs = [\n",
      "    (\"svc\", SVC()),\n",
      "    (\"KNN\", KNeighborsClassifier())\n",
      "    ]\n",
      "for name, clf in clfs:\n",
      "    clf.fit(df[iris.feature_names], df.species)\n",
      "    print name, clf.predict(iris.data)\n",
      "    print \"*\"*80"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "svc [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
        " 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2]\n",
        "********************************************************************************\n",
        "KNN [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1\n",
        " 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2\n",
        " 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2]\n",
        "********************************************************************************\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Try a [RandomForetClassifier](http://scikit-learn.org/stable/modules/ensemble.html#random-forests) (`from sklearn.ensemble import RandomForestClassifier`) and see how it does"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import RandomForestClassifier\n",
      "clf = RandomForestClassifier()\n",
      "clf.fit(df[iris.feature_names], df.species)\n",
      "clf.predict(df[iris.feature_names])\n",
      "pd.crosstab(df.species, clf.predict(df[iris.feature_names]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n",
        "/usr/local/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>col_0</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>species</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 50</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td> 50</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 49</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "col_0     0   1   2\n",
        "species            \n",
        "0        50   0   0\n",
        "1         0  50   0\n",
        "2         0   1  49"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##A Quick Decision Tree Example\n",
      "####[more examples](http://scikit-learn.org/stable/auto_examples/index.html#decision-trees)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import the decision tree library and train a classifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import tree\n",
      "\n",
      "clf = tree.DecisionTreeClassifier(max_features=\"auto\",\n",
      "                                  min_samples_leaf=10)\n",
      "clf.fit(df[iris.feature_names], df.species)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n",
        "            max_depth=None, max_features='auto', min_density=None,\n",
        "            min_samples_leaf=10, min_samples_split=2, random_state=None,\n",
        "            splitter='best')"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Write the tree to a file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.externals.six import StringIO\n",
      "with open(\"iris.dot\", 'w') as f:\n",
      "    f = tree.export_graphviz(clf, out_file=f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####We're going to convert it to a .png so we can display it in the notebook"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# you will need to install graphviz \n",
      "#(http://www.graphviz.org/Download..php) and pydot (pip install pydot)\n",
      "! dot -Tpng iris.dot -o iris.png"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image\n",
      "Image(\"iris.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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vMZMESIAESIAESIAEbpqAwUQCJEACUUKgePHixmuvvRb03pQqVcqoWrVqwPV++eWXhvqS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\nNVtxkPPMIQhhkppw5DbttNP6+SBsXhQvoXmfiLlv3nejmgmBmiOAii0L5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gBNwjjzziVlppJbfDDju4E088UYjUEIFvv/3WOycca6yxvKmDnBPWEFxlJQSEgBAQAt0WATH3\n3fbVq+FCQAgIASFQDAFswzm+De/4N954o45vKwZUJ8Lfffddj/Hiiy/uTR50rGAnwFRSISAEhIAQ\nEAKGgJh7dQMhIASEgBAQAgkEvv76a4d3d6TJw4cPdxNMMEHiqW5ricDjjz/uevfu7bbeemt32mmn\n1TJr5SUEhIAQEAJCoNshoKPwut0rV4OFgBAQAkKgGAK//fabW2+99dxPP/3kj7wTY18MqdqEox1x\n6aWXujPOOMOddNJJtclUuQgBISAEhIAQ6KYIiLnvpi9ezRYCQkAICIGOCGy77bbu6aefdsOGDXPT\nTjttxwgKqTkC/fv3d0OGDHF77rmnV8+veQHKUAgIASEgBIRAN0FAR+F1kxetZgoBISAEhEBpBI44\n4gh3+eWXe8Z+wQUXLB1ZT2uKwN577+2PyNt00029KcRiiy1W0/yVmRAQAkJACAiB7oCAbO67w1tW\nG4WAEBACQqAkAldccYXbfPPNvXo43vFFjUfgjz/+cP369XMvvviiwxZ/xhlnbHwlVKIQEAJCQAgI\ngRZGQMx9C788VV0ICAEhIAQ6j8BDDz3kVl55ZTdo0CA3dOjQzmeoHKpG4LvvvnPLLrusP52A9zLR\nRBNVnZcSCgEhIASEgBDobgiIue9ub1ztFQJCQAgIgRiBkSNHup49e7rll1/eXXfddTryLkam627e\nf/99f0Tewgsv7E0kdERe170LlSwEhIAQEAKthYCY+9Z6X6qtEBACQkAI1AiBr776yjP2k0wyibfz\nHn/88WuUs7LpLAJPPfWUW2GFFdyWW27pzjzzzM5mp/RCQAgIASEgBLoFAvKW3y1esxopBISAEBAC\nSQQ48m7dddd1v/76qz/yTox9Ep2uv1988cXdZZdd5s455xx3wgkndH2FVAMhIASEgBAQAi2AgJj7\nFnhJqqIQEAJCQAjUFoFtttnGPffcc+6///2vm2aaaWqbuXKrCQJsvhx33HFun332cTfeeGNN8lQm\nQkAICAEhIATaGQEdhdfOb1dtEwJCQAgIgQ4IHHrooe6qq67yjP3888/f4bkCmgeBPffc0x+Rx0kG\nw4cPd0j0RUJACAgBISAEhEA2ArK5z8ZFoUJACAgBIdCGCKDqvcUWW7izzz7bbbfddm3YwvZr0p9/\n/unWWGMNr2nBEXkzzTRT+zVSLRICQkAICAEhUAMExNzXAERlIQSEgBAQAs2PwIgRI9wqq6zidttt\nN6/u3fw1Vg0DAt9//71bbrnl3F9//eUefvhhN/HEE4dHugoBISAEhIAQEAL/Q0DMvbqCEBACQkAI\ntD0Cb7zxhlt66aVd79693bXXXutGG220tm9zuzXwgw8+8EfkLbDAAt6kYswxZVnYbu9Y7RECQkAI\nCIHOISDmvnP4KbUQEAJCQAg0OQKjRo3yR95NPvnk7oEHHnDjjTdek9dY1SuGwNNPP+2PyNtss828\naUWxeAoXAkJACAgBIdAdEZC3/O741tVmISAEhEA3QYCj7tZZZx33xx9/uFtuuUWMfYu/98UWW8xd\nccUV7rzzznPHH398i7dG1RcCQkAICAEhUFsExNzXFk/lJgSEgBAQAk2EwMCBA92LL77o1binnnrq\nJqqZqlItAmuttZY74YQT3L777uuuv/76arNROiEgBISAEBACbYeADNba7pWqQUJACAgBIQACBx98\nsLevv/322928884rUNoIgd13390fkcfJBzPOOKNbcskl26h1aooQEAJCQAgIgeoQkM19dbgplRAQ\nAkJACDQBAvfee69bYoklOnhPv/jii92AAQO8+vY222zTBDVVFWqNAEfkIcXHDv+xxx5zs8wyS0ER\nL730kht77LHdnHPOWRCuH0JACAgBISAE2hUBMfft+mbVLiEgBIRAmyPw3nvvuR49erg55pjD3XXX\nXW7mmWf2LcZpXp8+fdyee+7pjjnmmDZHoXs374cffvBH5P3+++/ukUcecZNMMokHBHX9TTfd1M0/\n//ye+e/eKKn1QkAICAEh0F0QkM19d3nTaqcQEAJCoM0QOP/8890YY4zh3n77bbfooou6J554wr3+\n+utuvfXW8070jj766DZrsZqTRmDCCSd0w4YNc998841bf/31veNEHO1x/9tvv7lnnnnGIcEXCQEh\nIASEgBDoDghIct8d3rLaKASEgBBoMwT++usvN+2007rPP//ctwwmn79ZZ53VTTrppO7+++934447\nbpu1Ws0phsCzzz7r/vnPf7oFF1zQPfroo3G0scYay+20007upJNOisN0IwSEgBAQAkKgXRGQ5L5d\n36zaJQSEgBBoYwTuuOOOmLGnmdhfI6l97bXXXN++fcXYt/G7z2rabLPN5uabbz6vvZF8jrr+BRdc\n4DgSUSQEhIAQEAJCoN0REHPf7m9Y7RMCQkAItCECZ599thtzzOwDXw477DC33XbbeRXtNmy6mpRC\n4P333/fe8lHBZ5MnTdjl33DDDelg/RYCQkAICAEh0HYISC2/7V6pGiQEhIAQaG8EPvvsMzfddNM5\nVPOL0eijj+569+7tbrzxRjfRRBMVi6bwFkcAT/loanz77bdFN3PoC8stt5wbPnx4i7dW1RcCQkAI\nCAEhUBoBSe5L46OnQkAICAEh0GQIcMwdDFspiqLIcUweXvRF7YvAkCFD3KhRo4oy9rScTaARI0Z4\nx4vti4RaJgSEgBAQAkLAudKrIyEkBISAEBACQqDJEDjzzDNLMnMw/px5/t///tf179+/yWqv6tQS\ngUsuucQdeeSRbpxxxilqpkF5mHBwuoJICAgBISAEhEA7IyC1/HZ+u2qbEBACQqDNEEACu8IKK2S2\nCgYOJu/www93u+yyi8NTuqh7IPDRRx+5vffe21155ZX+1IQs2/spp5zSffrpp/5590BFrRQCQkAI\nCIHuhoAk993tjau9QkAICIEWRiDLkR5M/Wijjea22mor984777jBgweLsW/hd1xN1aeffnp3xRVX\nuEceecQtsMACvj+k8/nyyy/dbbfdlg7WbyEgBISAEBACbYOAJPdt8yrVECEgBIRAeyPwzTffuKmn\nntofeUdLYeixre/Zs6c744wz3CKLLNLeAKh1uRCgT+CXYa+99ipwtDfGGGO4VVdd1Q0bNixXPook\nBISAEBACQqDVEJDkvtXemOorBISAEOimCFx++eWOc8shGLVpppnGXXXVVe7RRx8VY99N+0RWs9n0\nGTBggNfi2HPPPX1fQbsDVX0k95988klWMoUJASEgBISAEGh5BCS5b/lXqAYIASEAAh9//LEW7W3e\nFTbccEPv8Rxb+oEDB7ott9zSjTvuuG3T6mmnndYf8VevBsHUMk66G3344Ydu6NCh7qGHHvJN32mn\nndzWW2/d3WBQe1MIjDfeeG7eeedNheqnEBACQqC1ERBz39rvT7UXAkLgfwjMM8887rXXXhMeQqBl\nEZh77rndq6++Wrf6Y7bw3HPP1S1/ZSwEWg2BN998080+++ytVm3VVwgIASFQFIExiz7RAyEgBIRA\nCyHwyy+/uP32289tt912LVRrVbUSBHjH7SSpT7YdR4FXX311Mqjm9+C3++67u1133bXmebdKhpx5\nj2kHpyqIui8CI0eOdH369HGMCZEQEAJCoJ0QEHPfTm9TbREC3RyBySef3PXo0aObo6DmtyICU0wx\nRUOqPdlkk2mMNARpFdLMCPz666/NXD3VTQgIASFQNQJyqFc1dEooBISAEBACQkAICAEhIASEgBAQ\nAkKgORAQc98c70G1EAJCQAgIASEgBISAEBACQkAICAEhUDUCYu6rhk4JhYAQEAJCQAgIASEgBISA\nEBACQkAINAcCYu6b4z2oFkJACAgBISAEhIAQEAJCQAgIASEgBKpGQMx91dApoRAQAkJACAgBISAE\nhIAQEAJCQAgIgeZAQMx9c7wH1UIICAEhIASEgBAQAkJACAgBISAEhEDVCIi5rxo6JRQCQkAICAEh\nIASEgBAQAkJACAgBIdAcCIi5b473oFoIASEgBISAEBACQkAICAEhIASEgBCoGgEx91VDp4RCQAgI\nASEgBISAEBACQkAICAEhIASaA4Exm6MaqoUQEAJCQAi8/fbb7sgjj3SHH364m2GGGSoC5D//+Y8b\nd9xx3U477VRRukojv/POO+6OO+5w4403nlt99dXdP/7xj1xZ5En366+/uuHDh7vnnnvOLbfccq5n\nz55u9NEL96DzxKFCecrLVXFFahgCTz31lHvzzTc7lLfhhhu6McYYw7322mvu2WefjZ/TNzbaaCP/\n+9Zbb3U//PBD/Kx///5u7LHH9r/vu+8+d9ttt7lpp53Wbbzxxm766aeP4zHmHn/88fj33HPP7RZZ\nZJH4dzPc3Hzzza5v375+fFdan2uuucbNMsssbskll+yQ9L///a/77rvv4vAPPvjADRo0yI0//vg+\nTGMthkY3QkAICIGWQaBw1dQy1VZFhYAQEALth8AzzzzjLrzwQvfiiy9W3LgLLrjAXXLJJRWnqyTB\nkCFD3NZbb+1WWmklN/vss7tevXq5Bx98sGwWedJ9/vnnbp555nHvv/++L+Omm25ya621lvvrr7/i\n/PPEIXKe8uJMddM0CMwxxxzuxx9/dNtuu63bdNNN3WmnnebWWGMNz9hTyTnnnNPXdbPNNvObACus\nsEJc98GDB7uzzjrLLbXUUq53795urLHG8s/oC7vttpv7/vvv3dChQ91MM83kYGoDsTm1zDLLuBln\nnNFttdVW7tJLLw2PuvxKPRdffHG3zjrruJ9//rni+rBZsvnmmzu+K2lio2TNNdf0OIM1f2ycBMZe\nYy2NmH4LASEgBFoEgUgkBISAEGgDBEw6FR133HEt35IvvviiqjaY1DL66aefqkqbJ9Htt98emaQ0\nMkYhjn7uuedGU0wxRWQSvzgsfZMn3Z9//hmZpD4yZj5O/scff0QzzzxztO+++/qwPHGImKe8uJAm\nuqHv0ofrSSaVjg477LB6FlGTvG1jJ7IlVDT11FNH33zzTUGe22yzTeY4t82maPfddy+I+9Zbb0VX\nXXVVHGYMfjTJJJNEK6+8chyWvAH/PfbYIxnUZffvvfdexN8mm2zisfjqq68qqgvfg379+vm0Z555\nZoe0toES3X///b4MyrFNtcg2EHy8dh9rNPLVV1/12NhGagdsFCAEhIAQaGUEJLlvkU0YVVMICIHu\ngcCUU05ZVUMnmGACrypfVeIciY499livrpxUWUYqiCr0+eefXzSHPOlGjBjhHnroIS+xDRmhho0k\nFekt0tw8cUibp7xQhq7NicDaa6/t1cM/++wzt8suu8SVpJ+hybH33nvHYaVufv/991htn3gTTjih\nW3fddd3EE09cKlnVz4wpdldffXXV6ZMJ0TDgzzYcksG57/fff3934IEHZsb/9NNP3QsvvOC1b0I5\naC5g1gNprGXCpkAhIASEQEsgIOa+JV6TKikEhEA7IPDII494m/ojjjjC3XnnnW7UqFEFzYJxMWma\ne/LJJ+Nw7GBPPvlkz9S89NJL7qijjvKqw0l1dSKjRotqfj3oyy+/9Or3CyywQEH2MAOzzTabw643\ni/Kmu/HGG33ydP7zzz+/Z+yxl84TJ295WXVVWHMhcPzxx3szDdTkr7/+er/5w71JoXNXdK655iqI\ny5gxab5Dhb+WZFom7uKLL3bzzjuv23777WuZdVV5MVYwYZhvvvky05966qnezwAM/ayzzuouuugi\nZ1KqOK7GWgyFboSAEBACLYeAHOq13CtThYWAEGhFBFhQ33XXXe66665zjz32mOvTp49D2o6jq6OP\nPtpLzQ455BD/HAZmiSWWcDgJMzVkZ6r6fvGNtI37gw46yH344YcO6RzSQpieXXfd1dvLYhNfjB59\n9FEfv9hzwk0V3tsfJ+PgdAzGCIdkacJmmU0LmIPRRhut4HHedMGJWjr/4KzvjTfeiB2tlYpD3aup\nZ0Gl9aMpEGDjiH6NU8UddtjBO5hkk2ecccapqn4fffSR22effdzSSy/tll122arySCdCMwCm/phj\njvGbazvvvLPba6+9fLRqx1q6jEp/f/zxx+6GG27w2CWd5SXzWX755R11p444Exw4cKC7/PLLvaNM\nNGbyjEeNtSSiuhcCQkAINA8CYu6b512oJkJACLQpAiyyYSxg2mFOcASG92uc0ZmNeMwUH3zwwZ65\nDzDg8ArmHlVzpNpmU+wfLbbYYl6aCXPPYnzAgAF+I+Dhhx8OSTOvq666aoF37KxIaAYccMABBY9Q\nj4bwkJ8mHHD99ttvXgshbVKQNx3xaEfwbh7KCM69PvnkE5c3DmkrrWcoT9fmQoB+/u9//9ux6bXQ\nQgu5aaaZpqoK3nPPPV7N//XXX/fpYfQvu+yyqvIiEV7k0ZJhXJotvM97zz33dMn+X+1Yq7pSlpAN\nNjYXTjzxxJLZ8O3hD3r++ef9CQJghLbEfvvtp7FWEj09FAJCQAg0NwJSy2/u96PaCQEh0AYIwEz8\n8ssvXtoemoOHbnMWVnB8V5ZUMjCqHNEVCPVfvMonKStt8jn32Nqa072Sf2xCpAlbZSgtmScMzQHK\nnmyyyfhZQHnThXgFie0HeUMwdZXEqbSevhD9a0oEzPGZ1yS599573emnn15VHc2Bnj9Gj+MRF154\nYS+lTnrMz5spY/iUU07xpijm6NF7on/33Xe95D7J2JNftWMtb12y4sHUmwM+Z44Isx5nhrFp8vTT\nT3vNiCuvvNLH0VjLhEqBQkAICIGWQEDMfUu8JlVSCAiBVkYAxhx1ctTyAyGJRuV4ookmCkG5r0i5\nkzayeROyUVDub8wxOyp0YZsL4dguTRwxhn0vdUpT3nTEg5FHIpok8obYzMgbh/iV1pM0ouZDAMn4\nVFNN5ZAq02/ZeOIIt2oJ53Son0OYxlRKDzzwgNciYLOO4/qQcttpEZnZlBtnPM8aa5mZ5QjEdAWT\nH9TtUcvn75ZbbvEpOeKO32jAZBEaMjgxDOr4GmtZKClMCAgBIdAaCHRcxbVGvVVLISAEhEDLIIAk\nediwYW799df3nr5RNx45cmTMaDSqIf/5z386MNDpsjEZQKsgSSz28Q+Ac7804cQu6UE/+TxvOs63\nh8jfjjSLsyBvCOYeCS5UKk7e8nxG+tfUCGBff8cdd7i7777bn1mPXwo7ps5Ly7EVD+fYV9oI+tJ0\n001XlYo/qvZI6vGfgZQce3vU8QcNGtRhk67asVZpe0J8fHCgzYPvjUBhAxCHl2gqcNpA2mdFiMsG\nJJt0UJ7xqLEWkNNVCAgBIdBcCIi5b673odoIASHQpgggHcMxGBIyO2vb27k2uql2fnimVDtZD1R6\n08w9avfY/sMg4LDOzrv3SfAlgLQPh2JZlDcdeXOCAD4Dksw96sKoUcN05IlDvaqpZ1bdFdZ1CCCd\nh2nm5IjAxMO0Ipmmjxx++OG+v1RTQxxSYg6DQ8tqiLGLQ0v8X2AmcMIJJ/g/6suxfUGlvdqxVk2d\nSLPiiisWmP0QhgkOm3KMT749pQgP+XybII21UkjpmRAQAkKgyRGwnV2REBACQqDlETCV2+i4445r\nynaYunlkkq7IzmyP7Ji76JVXXolMAh0Zo1xQX/OGz3lUkTG6cbgxDT7MPM/HYf369YtMnb8g/Xrr\nrReZh/HI1HLjeLW8MYllNPnkk0cmBYyzPeeccyI7Nzz+HW7sHPLIGAT/M2862mlHd8Vt+vnnnyNj\n6iNj8EO2UZ44ecuLM22SG/oufbieZNLZ6LDDDqtnEZ3O2450jOx4xcjUyDvkZZ7d/VgwE5DIpPoF\nz21TKDKGuyDMnFVGJl2PzEwjDjdGNzLJe/w7eQP+ph2QDCp7T97G4EfmFyIyFf3ITAnKpqkkgjHl\nvs3mBT8zWXKspSNQN74n5sgzfmROBaPddtsteuaZZ+IwO2IzWmqppSJzjBmHtfNYo5GmCeSxefHF\nF+M260YICAEh0A4ISHLf5Jsvqp4QEAKtjwAS5R49enj13WRrkAKivsvxdRxJNXToUP/46quv9qru\nSAHDmdOoJSPdxu4XL/vYoyPB5MxuzqkePny4d9p34IEHeqlnOEYuWV5n7jn6asSIEY7jvpCoI+FH\nDfiMM87okC1H+OFFHDv6vOnw1I0N8lprreWlqtgHIyFddNFF4/zzxMlbXpypbpoGATzQI2XmLHpU\nyWeaaSaHCQuEp3vsxiH6Vf/+/b0KOic7BGm5f5j4hwkH4wOJ+sYbb+ymn35616tXL8dRcLUiNHIo\nY6eddnLnnnuuH8842+ss4ZMDB3ehzdj3b7755m6VVVYpyDo51rL8XhREth8//PCD/16cfPLJrnfv\n3v4oTtu0K9CSII3GWho5/RYCQkAItAYCo7FD0RpVVS2FgBAQAsURgHlmgW2SrOKRuugJjuJgVGGM\nR40a5Y+jM8m096gNg45qe1A/7qIqVlQstvBsTBSrMwwEjr3SHvTLpaMSMG7EK+XxO08c8spTHvGa\ngWCm2CjBo3u9CFtqvKlz5GK70RxzzOHWWGONDsfAYUaCKj6bXVmnKCRx4BtimiieQU+GV3LPsZDp\nIx0rSV9p3GJjrVQ+fI/YmGNjgg2PUtSOY432YvrBeDDJvZt//vlLQaBnQkAICIGWQkCS+5Z6Xaqs\nEBACrYjAFlts4ZZeemlnar/+L9kGJNy19JqdzLte9+ljv9LlFJOklktHPkgfSzH2eeMQL095xBO1\nBwLp0xZoFVoz5fpTaD2MbGepkYw9dS021kq1A18YbIbkoTzjkXw01vKgqThCQAgIgfojIOa+/hir\nBCEgBLo5Aqjco2YOg49Xaph5VNsfeeQRN9dcc5WVKHZz+NR8IVAWAZhcTqSYdNJJved6POubD4qy\n6cze3HvlR5KNg8g8acpmqghCQAgIASEgBLoIATH3XQS8ihUCQqD7IICXeWzrsfuFiUAVdvXVV/e2\nwFIJ7T79QC2tHwKc5V4NMf7CGDzllFOqyUJphIAQEAJCQAg0DQJi7pvmVagiQkAItCsCMA84C4Ma\nbZPbrpiqXUJACAgBISAEhIAQEAKFCPx9WHFhmH4JASEgBIRAnRBotE1unZqhbIWAEBACQkAICAEh\nIASaDAEx9032QlQdISAEhIAQEAJCQAgIASEgBISAEBAClSIg5r5SxBRfCAgBISAEhIAQEAJCQAgI\nASEgBIRAkyEg5r7JXoiqIwSEgBAQAkJACAgBISAEhIAQEAJCoFIExNxXipjiCwEhIASEgBAQAkJA\nCAgBISAEhIAQaDIE5C2/yV6IqiMEhIAQaDQCv//+uxsxYoQ/J3yVVVbxx/Q1ug6VlnfzzTe7vn37\nZp5L/s0337jzzz/fHzvYr18/t9JKK7kxxhij0iIUPycCHPPI+fA77bRTzhR/R3v77bfdkUce6Q4/\n/HA3wwwzVJS2ksi//vqrGz58uHvuuefccsst53r27OlGH728bOP77793V1xxhXvnnXfc7LPP7jbd\ndFM3/vjjFy161KhR7pxzznH7779/0Tg8uOaaa9wss8zillxyyYJ4P/zwg3/27rvv+joyFscaa6yC\nOJRB3+dIzQUXXND16dPHTTjhhHGcn3/+2d10003x7+TNBBNM4NZaa604qFxeISJHeX733Xfhp/vg\ngw/coEGDCrDg+/Hwww/7sN69e/u6xQlSN59++ql77bXXXK9evVJPnMuDQTpRMTzT8fRbCAgBIdAd\nECg/u3UHFNRGISAEhEA3RuDFF1/0TMVJJ53kPv7446ZGAkZj8cUXd+uss46DkUnTV1995Z8///zz\n7qWXXnKrrbaaW2aZZdLR9LuGCHDM4yWXXFJxjs8884y78MILHf2vXvT555+7eeaZxzPDW2+9tWd8\nYXD/+uuvkkW+/vrrbs4553QnnHCCO/HEE922227rGVYY02L0r3/9y5188snFHvvwp556ym2++eaO\ntieJ8hZZZBE3zTTTuH322cd9++23fkMBpjkQmxMwxPPOO6+PM3LkSLfsssu6Tz75JERx1113nd+E\nYCMi/XfeeefF8fLkRWSY8DXXXLMgr2effbaAsYfRv/jii91uu+3mN9w23nhjd9ppp8VlhZsvvvjC\n7bXXXm7WWWd1N954YwiOr3kwiCP/76YYnul4+i0EhIAQ6DYIRCIhIASEQBsgYJKw6LjjjmuDlnRN\nE4wZjmzii84999yuqUCOUt97772Iv0022cTX1Rj5DqnOPPPMyCSScbhJhX3chx56KA5rxhv6Ln24\nnjT33HNHhx12WM2LMGlr9NNPP1WVrzF8VaXLk+jPP/+MTFIfGTMfR//jjz+imWeeOdp3333jsKwb\n2xSKGBOQbRBExrj7fmQbBFnRI5PYR3PMMUc09dRTZz4nEJxMk8TnQz9NEuVts802yaBoq622iv75\nz3/6MNqy0EILRcb4F8Qx6X9kEv44bL311ovuu+++yLQOItNYiP/I56KLLqooLyLbpkZ0//33+3HH\n2DONgcg21eLyrr/++micccaJkmPxtttu8200SX4cj5snnnjCY8p3Ztdddy14xo9yGKQTlMIzHTf9\n+9VXX/V1tI2l9CP9FgJCQAi0NAKS3HebbRw1VAgIASFQHIExx/zbSmu00UYrHqmLn8w000yOP2OC\nM2vy22+/ecnh5JNPHj/fcsst/f3EE08ch+mmtgig7j3eeONVlemUU05ZVbo8iZB626aOl7qH+Jhn\nGNPsJcs//vhjCC64Pv30026zzTaLVcunmmoqbzqAKv8jjzxSEJcfb7zxhkOavcYaa3R4lgxAXf/A\nAw9MBsX3SN9ffvnl+Dc3xjQ7TAqgxx57zKGNgnQ/Saj233333Y460//3228/h1o8qvpjjz22//v6\n66+dMdaxSn6evCgDLYUXXnjBaxCEsTfjjDMWmMKcddZZfjxONtlkcbWCucExxxwTh3GzxBJLONtg\nKghL/iiHQTIu96XwTMfVbyEgBIRAd0FAzH13edNqpxAQAl2OACrCqCEPGTLEq89icxwIFfPbb7/d\nHXXUUe7YY491H330UXjkrzy/6qqrnElIHTa5Z5xxhlcxNomef/7ZZ585k7p7W/OkfaxJKt2dd97p\nmRziYBMMA/D4448X5F/sB2r6qF1jF33vvfd2iFaqTR0i1zkAZqZHjx4FpcCcwHQtsMACBeH6kQ8B\nbKBNyuwZKfoBpg6hz4Uc6AM8C0Sfg+Gkv9Bfr776at9/YIKThGq8SYXdk08+mQyu2X1Q/U6/+/nn\nn9/B2JuEObMsNo9QaU/StNNO6xZbbDGXZGJ5jr+Kgw46yI/pZPz0PXVBzX+++eZLP/K/TeLuGfjL\nLrvM/wZ30uy+++7+NyrrkImT/DX8g2GG2MSg/4ff4TnXG264wS2//PJx3fPkRbpTTz3Vfydg6FGl\nN8l/h/JR20/XaYoppvDjkDpVQuUwSOZVDs9kXN0LASEgBLoTAnKo153ettoqBIRAlyGAk7fVV1/d\nPfDAA17KucUWW/i6sGhmIY9Ei4U9jDcSL2xpTXXUx8UZGDa/b775prcBZnE+ySSTuL333tvblK+6\n6qo+X5guGCkcbt1yyy3uww8/9HawLO6xM+a5qSR7pgFbYjYL+vfvXxQTGK8rr7zS7bjjjm6iiSby\ndu5Iwk8//XSfplSb0pmySZDczEg/5zdaA7S7FgTDce211zpTQ/ebG7XIs7vlgcQX53PYavPe6bOm\nOu4ZSN7T0KFD3aWXXupMxdrbYGPTThoc69G3kH7D9CP55jdSXjYH0Kx45ZVX3CGHHOI3udg8yGJK\nwfvRRx/tsJmQfg/0aRjQNDFeIBjzJP3jH//wP9ObDSEOzGkW4Ugu7TSQTS8YcMZHMaLvMwbBKrnx\nloy/3Xbbucsvv9xjjD0+Uvyzzz7brbvuuj5a0IzAxtzMUuKks802m7/Hwd/HhNUAAEAASURBVF4x\nwg5/ww03jB/nzYsNATYveAdsBg4cONDX8Y477ogdVOJgEBzxEcA3KRD1uueeexxOCUthE+JzLYdB\niJsHzxBXVyEgBIRAt0OgpY0KVHkhIASEwP8QMGlbU9vcmxQsWmGFFeL3ZYxuZJ64/W9j6iNT+Y1M\nDdb/NmdX3h4UG9VA5pHchxnDGoIi2wjwYdi9BjK1X28Da4y8DzKnWz7OBhtsEKL4cozhisxDeWSL\ndx9uzISPZ4yc/43Nrm08eDvhkBCbYJskI1vs+6BSbQppwjXUn/TF/swzeIhe8mrquD6PpJ1vMgG2\nuNgKG+Ph40066aTe3jcZp9num9HmHpyxTw9kqt8eT3MwF4L8FTvvpK05Ntm8Y1MPj/uXbTb5sFtv\nvTVOa1oVPixtfx5HsBszp/BxivUZwk3bJZkkvl900UUjU8OPf4cbxhXpdt555xBU9mobbH68MC4C\n2UZddOihh4af0R577FGAAw9MO8H7iAhj25jgom3Gtt+YYv986aWXjr8H5IOtu0nmI9Me8HkSBpmD\nSR//lFNO+Tsg9d+0dXy6UD6Pq8mLbxI+G8DNNh/jUmzjz4fxfpNkmzWRbeIkg/w9fgDII8vmngil\nMOB5JXgSvxjJ5r4YMgoXAkKg1RGQWr7NMiIhIASEQL0RQDKPBB5P2XiNRn0cNVQISRwSTWOQ3C+/\n/OLjER4kj9wHqVhSxXiuuebikTNHW/7KP8rBThfpFoQ9NLTwwgv7K/8oB00AJPsc85VFSOwxBcBz\ntzFB/g8bXCRyeOmGSrUpnecuu+ziVbRR0y72h/SvFkSbMT9Aaoinc65piWstymn3PN566y3fV7Hl\nhuhnYIsEO0nYhieJY/HQwqCvBF8OeHiHkhLmdLpkHuGePlesv4Rw+mgWJY+ISz4PZgV4ps9DxD/4\n4IO9NkzIE60VPMIXs6EP+dL/GN+MuXLE8Y22AejQgEBavtRSS8V4oZnAsYHY1iNBx6QA7Ru0H6Dk\nNyBZDurraF8ky68mL/KnbI4s5NsQiPJ5z0jd0dJAQ4HvBScgFKtTSJt1LYUB8SvBMyt/hQkBISAE\n2h0BqeW3+xtW+4SAEGgKBFZccUV/DBQLclTmOTKLRTqEoy4W3zAQMEZBRbnccV1ZzFE4F7uYs7AA\nBva/EBsN5uU7BMdX1IJRZw4q+PGDxE2pNiWi+VuYvMDopZ/V6ze4ojKNEzSYDjY9sjCrV/mtni+O\n2ThDHNtp3jUq9zD6nL9eKeHIDjKJSEVJgwp5RYn+FxkmFsY8/d7Z7IHChsP/ohe9cHzb4MGDC5zZ\nmZTej1PGciA249ico6+ZtohnhFGJJz1hEBsSEA74CDMJvR9n+OLApAb/A4wTzB623357zyibtoNP\ngxkOzuruuusu/044cg7neJSbdrTnE9g/TFOyTG+qyQsV/LXXXrvAvwLfLZh+TA5w+Lfgggv67xo+\nQeg/lVA5DFD/z4tnJeUqrhAQAkKgnRAQc99Ob1NtEQJCoGkRgNE8/vjjXZ8+fRznQiOdwxGZHcnl\npee9evXyjDTO34rZAqcbV8qzfaln5GPHWvnssPnPIpgxbPuxuQ0bBul4pdqUjgvTgg1uKaLMYlLY\nUunKPVt55ZW94zYx9uWQKnzOue1oaeBzAakxPhjwB4GPh0aRmXPEHuOLlYm0e5lllunwmPPtITQN\nZp999vj5l19+6e/zMPdogMA447MiSWyK4TQwSWiewLzjgwDHeYxtNBX4HShsbrBpYir13gEmm2ic\nE29HwcUbYHwfsK9Hko2WAJsFEG3lD0Lrhs0FvitZdu20E20hmOYsqiSvkB5tnbAxGMLQKuKbFggp\nPhJ+NkQqoXIYoGmUF89KylVcISAEhEA7ISDmvp3eptoiBIRA0yLAIh1JPVJPpHYwC3ijhgEwu13P\nRIejtMpJ7GvRSDsL23v/LqaajEot0n+coKFSHwhGw3wFeDX3Um0K8cM1SN3C76wrEst6MPdoIay5\n5ppZRSqsBAK8DxhP1K05so4+2+gNkptuusn3wxLV9FovWcw9zv+OOOIIZ+etFzD3SJoxU0kzqeky\nUGmHGQ/HKYbnMMzDhg0LP+MrffeSSy7x5i4hEIY0STD/mDawSbLDDjvEjzjVIb3ZgJQcZ4OcchGY\n+5AADYqNNtrIYZpTzOSE+pvfgUxngyEfrnnyCvHJk3oVI55zagdaCMEkqFjcdHg5DNAeyYtnOm/9\nFgJCQAh0FwTE3HeXN612CgEh0KUIoDqLpK9v377es/g666zjvZBTKZhoznjGjha1W1RaIezmg9Qu\nqBKjYhwIL/uQOZbzdq/cB3V81IOThA1sII7ZQ5KeVCkO9u4hTxgHjvhCpZi82HggD9RiYeqhUm0K\nZYUrntP5qwWhHg6l24iPACS9MB8cdwaNGjXKb6YE1WYfqH+5EICx5H1zBBwMIFJTNoPSUmL6JP2H\nI/DYEKAPwRSTJlCQlvOOAoW+HJ6F8OSVs+qrJeqKRBnJNgw62iz0GfoCduNongSCMWcccTIAhJYJ\nR1biIwPbeggVf7z807eC9Nw/qME/vgcwxpQV6oXKPWruabMZxjgMPX472CAE8ywqppKfjFssLzbj\n+A5ttdVWsco/m2TE57uQRZhvcNoHjH3SO38ybrGxS5xKMEjmqXshIASEgBBIIGATsEgICAEh0PII\nNLu3fLOn996m8TCPl3y8RduRVx53swn3XslNKhrZ0VfemzVese1M7chUaiOemyQdY+XIFtsRnvZN\nRTrCGzhh/fr1i/B2TzxznuXDbHEd2QI9sk0D/9uYkQhv93hAJ++kh3075iqyTQcfz1SQI9tk8PUy\nRiYy6aYPpxxjauI6E6FUm3wGNf6Hx288tdtRZr5OxrBFZn8cl4KXfOpvTFyEt+5///vfkfk2iGxj\nJI7TrDfN6C3fmM3IpK/x+6cP8GdmDr5fmRQ6wku7HR3nw41B9n2Tvk08Y64jvOPbZpLv14TRj03d\nPDLGNVp//fV9PPqVScLr8mrwrm7aMZFtTvm60v9Nut6hLFM39/3KNigik+xntpv6m0+MyDaMOqQn\nwOzYO3jLT0c05ti3OX1CAOGMT7A46aSTIjOJiExTwuMZ8rBNkMg21iLTUojMXj8EZ16Ja0x/xGkZ\nWVQuLzAwdXtfV049AEPb7Ih450kCX74fZkYQ2UZIRL7FiO+KbRr6PBnDJuH3/SjEz4NBiBuuxfAM\nz4td5S2/GDIKFwJCoNURGI0G2IQlEgJCQAi0NAJIsZBm4SiqGSlINbGzR7U5eL8PdUUVH6lmUGXl\n04y9ux1/FaJUdcXbOKrVdlyYdy6Hiq9thHgpZt4Msc9H6jnTTDMVJCnXpoLIDfyBtgO44QCsVQjp\nMpLSYqcX1KId2KDjuR3HjXkITRO0PJZbbjkXvNYbM+Wl+ZzagJS2VQipuzGeBV7jk3VH24DxZhtq\nyeCG36O2z3hD6yBdF0wUkOQX85ORrCzviXzSqv4hTp680KxAW4NxNP3004ekBVdjkr1TzsUXX7xm\n460UBgWFd+LHa6+95hgPaCOhiSESAkJACLQLAtm6XO3SOrVDCAgBIdAkCATVWZNYZdYIVdzA2BMB\nZrqzjH26IBbpbIJUSnbWeWaScm3KTNSAwLR9cgOKbLsisEsfMGCAZ+5wdJh0SBe86LdSo2lD8ji4\ndN3DEXfp8Eb/ZowGR4DpslFbz0t8S4ox9uSRJy82IdMmAenyqWux+qbj5v1dCoO8eSieEBACQqC7\nIiDmvru+ebVbCAiBboEAUjAIabZICORFAOdm+IHABp3TBtjgeffdd90TTzzheGbq7XmzUjwhIASE\ngBAQAkKgQQj8vzeZBhWoYoSAEBACQqAxCMCMHXLIIb4ws7H3R2IlnZw1phYqpRURQGo/dOhQd9VV\nV/lj3dCG2GKLLbyzvMMPP7yDWUkrtlF1FgJCQAgIASHQbghIct9ub1TtEQJCQAj8D4HpppvOe9PG\no3agYmfWh+e6CgEQwCyEc8r5wxZd/Ub9QggIASEgBIRA8yMg5r7535FqKASEgBCoCgFs9mttt19V\nRZSopREQY9/Sr0+VFwJCQAgIgW6EgNTyu9HLVlOFgBAQAkJACAgBISAEhIAQEAJCoD0REHPfnu9V\nrRICQkAICAEhIASEgBAQAkJACAiBboSAmPtu9LLVVCEgBISAEBACQkAICAEhIASEgBBoTwRkc9+e\n71WtEgJCoM4I4GRsxIgRbtiwYW6VVVZxq6++ep1L7Fz2t956q/d0HnLp379/Rfb4v/76qxs+fLh7\n7rnn3HLLLed69uzpRh+99vvDN998s+vbt68bd9xxQ1XjK8f5nf9/7Z0HmBTF1oYPWSXnJElBkqjk\n5EWCYiDDRUCCXBAVBK8KEkQkCApXQEQlh58oGSQHSUpOEkRASYISRQQkCvRfX93bzWzvxN2Z2Z6d\n7zzPMN3V1RXe7hr21Dl1avx4vfd6rVq1pEaNGoL9y0Mlgfb56NGjsnXrVqs5RYoUkZIlS1rnPEj8\nBE6cOCFLliyRnTt36m0EndrjK1euyPTp0+XYsWNSsGBBeemllwT7y9sF7zPGPcYZfjPy589vz8Jz\nEiABEiABBxEI/l9mDuocm0ICJEACoSKwb98+mTVrlgwbNkxOnToVqmqCVi6ino8aNUrKly8v1apV\nCyj6+blz56Ro0aJaqW7Tpo0sWLBA6tatK3fv3g1a+6AQlSlTRurXry/Xr1+PVe4ff/yhr+/Zs0d+\n+OEHef7556VSpUqx8gUrIS59zpYtm25Tnjx55OWXX5YpU6YEqzksJwII/PXXX7Jx40bp37+/LF++\n3LEtPnTokDzyyCMyZMgQ+fTTT6Vdu3by2GOPyZkzZ2K0Gb8Z2GkDij8mL7t27SqNGzcWwzBi5OMJ\nCZAACZCAcwhQuXfOs2BLSIAEIohAqVKl5I033oigFougzQ899JDkyJFDb3XmT+OhwMNiV6JECXnl\nlVckS5Ys8vHHH2sF+7333vOnCJ95YO1E+VA4PAkmUrZt2yaTJ0+W1atXS58+ffQ5lKlgS1z7nCZN\nGsmXL5/2bMidO3ewm8XyHE4Az79Zs2Z6As3JTX377bdlxYoV8tNPP8mvv/6qx/WRI0ekZ8+eVrMx\n1qD4Y6w/+OCDenJv0KBBMnfuXFm7dq2VjwckQAIkQALOIkDl3lnPg60hARKIIALJk/93ZRP2BE+s\ngqUHGzZs0NY9s49w0YVl+osvvpCrV6+ayXH+zps3r+DjyeX31q1b2lU/U6ZMVh2tWrXSx+nSpbPS\ngnUQjj4Hq60sx3kE8Lvg1N8ELBdo3ry5ttSDXNasWaVfv356ic2mTZssmKY30o8//milpUqVSh9j\nuQqFBEiABEjAmQS45t6Zz4WtIgESCBEBuM6OHTtWoDBizTjcux999FG5fPmyTJo0Sa5duyYNGzaU\nQoUKaffwdevWya5du/Sa05YtW4o3iyzWtcMCBgserNxY1wpLM9bn58yZU5o0aRKjV998841eo50x\nY0Z9LXPmzDGuO+Fk/vz5uhmwrLsKmEGxX7p0qXbVdb0W7OOUKVNKgQIFYhS7d+9eqV27trb4x7gQ\nhBMn9DkI3Ui0RcAt3Iz/gIkmxDZA3AtTsKzD27g9cOCAdkF/6qmnZNmyZQI3dbibYzkFvDbgDbJ5\n82apUqWKji1hlgsr98KFC6V9+/a6fli/8XvQtm1buf/++81sHr+9jXdfffJYaIAXMIEGDx5XwW9T\n6dKlxZysxLWaNWvq37EPPvhAypYtK5hYwzIT/A5gWQ+FBEiABEjAmQRouXfmc2GrSIAEQkQAijcC\nwsEFFe7dUFIhsABDiTx58qRW7DEJAAUff7R3795dbt++LZUrV3a7Htxsap06dXQQrb59++qktGnT\nCizMvXv3ls8++8zMpicWsM71999/1woq3FyhoLhayazM/zuAsgELurcP2h5s+fnnn3WRUABcBevL\nIXDtDadACYKLPp7JyJEjQ1K10/ockk5GcKHvv/++HD58WN566y2pWLGi4NwUb+MWk21dunSRYsWK\naa+TTp06aUUeMSQweYSJqhYtWgiCOmKt+T/+8Q8rQOK0adO0tRv3d+jQQSu6mGBCGVWrVtUTeGYb\n7N+YSPQ13r31yV4erOrefgdwzdNyFUwguvMqwG8HJjpNQXC9Dz/8UC99gXLfq1cvQZyRNWvWuA12\nad7HbxIgARIggQQmoP5QopAACZBAxBNQFinjP//5j9/9UG7lhvoD1lAR2K17lLXdOH78uD6fOnWq\noSz7hgoypc9VlHhEkTLUWlQr//79+3XauHHjrLR//vOfhlqjap3jQFnKDKWEWGmDBw82lMJvnas/\nrHU5Kkq8lWY/UJMPOg/a4OkzYMAA+23WuYqIbShlyDr39wBtV9bRWNnBAe1QcQdiXYtrQo8ePXSZ\nKnie2yKU4mYoJUk/N9SdIUOGGM/D7U1xSAxGn/E+qrXNfteOdxf3hFLUBJKhJp5CWUXIy1aWdUPF\nfTDUhJhVlwpgZx37M27Tp09vKIXVUF46+j7ltWOkSJHCUMEmrTTllWKoyT7DtWyl+BtKMTZUQEer\nPqX06ndWBau00pQXQIzfAF/j3VefrIL/dzB06FCPvwHmbwP6468oLwjdXjX5EesWFXRP16Ws+oba\nqSLW9UhNUN4bul9qwiJSu8B2kwAJkIBbArTcq/8JKSRAAtFHAMHw4IKvlAHdeVj18EFANAgCYyEq\ne/bs2eXGjRvaDRfpplUXx3EV9ce5fP/99zogH9qBoFWFCxcWRIT3JIhkjfZ6+yCadbAFng7u5M6d\nOzoZwfnCJalTp5YxY8bo54RgX3hesKIGW5zU52D3LdLLg9UZYwVLXGBhh8Caboo/4xZeOg8//LDl\nSg8Pm1y5clmeOigLlmu46WOrOFPw/sF1vXjx4maS9iBBGuI0eBJf491Xn+zlwlvA2+8Arl26dMl+\nm9tzjGO43mO5gf29x9aOCKA3evRovTYfyw9MryS3hTGRBEiABEggwQlwzX2CPwI2gARIICEIwNUU\nH/zhCgV7xowZOtCU2Rasx4dijz98sec68kKwJjc+gr3a4VaLNflw4/dX/FnT629ZgeSDggMFAEG0\nzIBauB+KNQQuzuEWPBu4ZCMA2Lx582K1Lb7tcWKf49unxHQ/AjlijTy2TaxRo4bAZR5jFRLXcev6\nbpuslPXbZ8BITAIgmvz58+fN22J8+zvevfUpRoHqBJMJ+ARDMDGCLe9KliwZozhlDtJsldeB3i0D\nrOvVqyd9+vSRWrVq6W0pY9zAExIgARIgAUcQCM7/Do7oChtBAiRAAoERgFLfunVrHTwLgbVmz55t\nFQCLHdbSfvnll3pdfLDWlkP5gGD9aiDKPax/vqJUI0BYsPd+x/72EKzJVa79+hj/IF4AJCGUe12x\n+ufpp5/W23K5U8zMPHH5dnKf49KfxHbPE088oYNcIu4CJucQIA7jCUHf4jpu3a1DBzdP6SZTjEl4\n1aglNWZSjG9/x7u3PsUoUJ1s375dEJzPmyDQoC9PHnjBQKmvW7durKIQsBABBJ977jl9DTE2MJGG\niQz8TpYpUybWPUwgARIgARJIeAJ0y0/4Z8AWkAAJJBABuPYiwBT2fX7sscd0RHyzKbBQIco9IrJD\n/LXYw6IGN35PApdgBO9CMDhE9XYVLBHAnu/uBEG/5syZ4/Vz8OBBd7fGKw2uuFCe7QG6sKUWFBJv\ne9PHq2I/blYxDwKaIPGjSJ3FyX32tw+JNR+UaURthys9Jt6WLFkip0+f1oon+hzXcRtXXgh0ifFu\n/k7Yy/FnvPvqk71MTDT6+i2AO703wY4QsM6bW0qaeaHUQzBZgt8800MHaQiqWa5cOY+/UchDIQES\nIAESSFgCVO4Tlj9rJwESSEACcLeHIrdjxw7tJu/aFGzzBqUBEbRhpR4xYoS+DJd6uNpCzHWtiNBt\nCraQQv6JEydql158X7hwQbB+9eLFizrbu+++q61i1atX11t2Yf09IuqjPOz37k6wphcKtbdPmzZt\n3N0arzSsqe/YsaN88sknWhlAYVBmsO2fCrCl3aDNChCjANtkue6XbV7z59vkY58cwSSIChaoYyCY\n5YApuGHtvavAWoklD/GRQPocjPri09ZouxcKqQpeZ72LGG8qwJ7+gIWvcYv7kcfuBYMxbI95gXz2\ndxG7ZmArPVOgRMNjxlW5xzjGvagL4mu8++qTWZf5jX3qvf0O4NrWrVvN7LG+YfUfNGiQnrzEcgB8\nsJvHa6+9JtgBAAKu2D3E3BYSaegTxrgKGopTCgmQAAmQgBMJqP9UKCRAAiQQ8QQCjZZvdli58Rpq\nPal5an0rBdVQwfUMZbU2GjRoYCiLuqH2gjbUnvSGUtgN9cezgej26nfdUK6thpoE0PcqS5dRoUIF\nna7cuw3lymo0bNhQ5x07dqzOg+jYiAyvrPw6H76Vi7Gh1rZb9Qf7IK7R8tEOtLdbt26GUmCM4cOH\n67ZPnjw5VhNV3ALdH7WNWKxr3hKwI4FS0g3l+qvvV9ZEY+XKldYtiJIPxspFWkc5R4RypYwYYG0X\nRIRHOUoJs18K6NzfPnuqj9HyA8Ltd2Y10WMoC7LRtGlTQ7mHG2rSyVBxMaz7vY1bNUFnqO3d9DuW\nNWtWA+8r3iHcj3GsvAEMvLsqIJ0xcOBAnYYdGSZNmqTLV8qv3jlCTXYZSmHXbVBLawxE24egbXiP\nVXwMfS/KPXv2rB4/3sa7rz7pwoP0j1L8DRUYULcPfXb9qMlOQ02aWTUtX77cUMEDDbV0SfdLTdzp\n8W9liOADRsuP4IfHppMACXglkARX1Y87hQRIgAQimgBc3RE5HVayQAXRpREYyy5wS4XVGFGyIfi5\nhKs+LFq+BAG2lAKhs8H6By8Bu6BsWPTRdnf12/PH57xQoULaumi3dAdSJgLrwSvBDF7m7l6szUdA\nulAIPCbA3hsrWGDxjNQkTFCa4KvPnurDM1WTQoJYCf4IPCPgHYI146ESxBJANHkEiYxkgfUcYxNr\n3d15usRn3Hrj8vrrr8uECRME+9bjPVdb6gnc7v0Vb+PdV5/8rSPY+fCb99tvv2lPBzVhFWPpUrDr\nCmd5WMKE8YDlB48++mg4q2ZdJEACJBBSAgyoF1K8LJwESCASCHhSFhEMy1Ts0Q8E1/JHsUdeU7HH\nsTvFHumIgO+6rRbSQil2V+RA60KQLm+KPcoLlWKPspUVFV9exb6dl9fMflz01WdP9WFSgBIaAmak\neHeKPWqMz7j1t8Vxec+9jXdfffK3XcHOh988BNGjkAAJkAAJRAYBKveR8ZzYShIgARKIFwEooYsX\nL9YKMoKRIYigp0mHeFUUxTdjPbJyZdYBx5SrNvkmsncBHj6wsMNbw9OkTiLrMrtDAiRAAiQQYQSo\n3EfYA2NzSYAESCAuBBB8jhJaAnDvNV18VWyC0FbG0sNKYNq0aaLiQOilOSr+hLRr107vFhHWRrAy\nEiABEiABEvBBgMq9D0C8TAIkQAIkQAIkEN0EEA2/Vq1aFgRsD0khARIgARIgAacRoHLvtCfC9pAA\nCZAACZAACTiKAILnUUiABEiABEjA6QS4z73TnxDbRwIkQAIkQAIkQAIkQAIkQAIkQAI+CFC59wGI\nl0mABEiABEiABEiABEiABEiABEjA6QS4z73TnxDbRwIk4BcB7CuO/c1z5MjhV35mIgEnEcCe7SlS\npAjpPvfFihWTS5cuSc6cOZ3U9bC35erVq3pbS09bYIa9QWGsEJH+se1etO+UcePGDdm/fz/3uQ/j\nu8eqSIAEwkOAa+7Dw5m1kAAJhJhAv379BFuRUUggUgmYkfZD1f5evXrJ7t27Q1V8RJR75coVmTJl\niuTKlUtq1KgREW0OZiOnT58umNxo2bJl1Cv4jRs3lkKFCgUTL8siARIggQQnQMt9gj8CNoAESIAE\nSIAESCDUBK5fvy5VqlTR+9Rv2bJFojFI3unTp6Vs2bJSokQJWbJkiSRNytWZoX7vWD4JkAAJhJMA\nf9XDSZt1kQAJkAAJkAAJJAiBtm3bypEjR2TRokVRqdgDOpZkzJ8/X9atWyfdunVLkOfASkmABEiA\nBEJHgMp96NiyZBIgARIgARIgAQcQGDBggMyePVt/ChYs6IAWJVwTYLkfM2aMDB48WKZOnZpwDWHN\nJEACJEACQSfANfdBR8oCSYAESIAESIAEnEJgwYIFgngDw4cPj8p19u6eA9bc7927V9q1ayeFCxfW\nrvru8jGNBEiABEggsghwzX1kPS+2lgRIgARIgARIwE8CUGArV64szZs3l1GjRvl5V3Rku3PnjtSq\nVUsHIt2xYwd3GomOx85ekgAJJHICVO4T+QNm90iABEiABEggGgmcP39eW6Tz588vq1at0lsNRiMH\nb33+888/pVy5cpIlSxZZu3atpEqVylt2XiMBEiABEnA4Aa65d/gDYvNIgARIgARIgAQCI3Dr1i1p\n2LChJEuWTObOnUvF3gO+DBkyyMKFC/We7+3bt/eQi8kkQAIkQAKRQoDKfaQ8KbaTBEiABEiABEjA\nLwIdOnSQPXv2aMU1c+bMft0TrZmKFCki06dPl0mTJslnn30WrRjYbxIgARJIFASo3CeKx8hOkAAJ\nkAAJkAAJgMCwYcNk4sSJMm3aNClevDih+EEAa++xo0Dnzp1l9erVftzBLCRAAiRAAk4kwDX3Tnwq\nbBMJkAAJkAAJkEDABFauXCkvvPCCVlS5j3vA+KRZs2YChtu2bZOHH3448AJ4BwmQAAmQQIISoHKf\noPhZOQmQAAmQAAmQQDAIHDp0SCpUqCC1a9eWKVOmBKPIqCvj+vXr8uSTT8rNmzdl8+bNkjZt2qhj\nwA6TAAmQQCQToHIfyU+PbScBEiABEiABEhBEfS9fvrxkzJhR1q9fz6jv8XgnTp48KWXKlJGKFSvK\n/PnzJUmSJPEojbeSAAmQAAmEkwDX3IeTNusiARIgARIgARIIKgHs1/7iiy/KtWvXZMGCBVTs40k3\nT548Mm/ePFm2bJn07t07nqXxdhIgARIggXASoHIfTtqsiwRIgARIgARIIKgE3nnnHdmwYYNW7HPk\nyBHUsqO1sMqVK8sXX3wh/fv3lzlz5kQrBvabBEiABCKOQPKIazEbTAIkQAIkQAIkQAKKwLhx42T4\n8OEyc+ZMKV26NJkEkUC7du30doKtW7eWQoUKyeOPPx7E0lkUCZAACZBAKAhwzX0oqLJMEiABEiAB\nEiCBkBL49ttv5emnn5bu3btLv379QlpXtBZ++/ZtqVmzphw7dky2b98uWbJkiVYU7DcJkAAJRAQB\nKvcR8ZjYSBIgARIgARIgAZPA8ePHpWzZslKlShXtNs6gbyaZ4H///vvvUq5cOcmbN6+sWrVKUqRI\nEfxKWCIJkAAJkEBQCFC5DwpGFkICJEACJEACJBAOAn/99ZdUqlRJkiZNKhs3bpTUqVOHo9qormPv\n3r2aeatWrWTEiBFRzYKdJwESIAEnE2BAPSc/HbaNBEiABEiABEjAImAYhrRo0ULOnj0rCxcupGJv\nkQntwWOPPSaTJ0+WUaNGyZgxY0JbGUsnARIgARKIMwEq93FGxxtJgARIgARIgATCSaBnz556izZs\n1QY3cUr4CDRs2FA++OAD6dixo3z33Xfhq5g1kQAJkAAJ+E2Abvl+o2JGEiABEiABEiCBhCIwffp0\nad68uUyYMEH+9a9/JVQzorpeeE40atRIL4dAgD1OsET168DOkwAJOJAAlXsHPhQ2iQRIgARIgARI\n4B4BKJIIntehQwcZMmTIvQs8CjsBxDyoWLGiDqy3YcMGeeCBB8LeBlZIAiRAAiTgngCVe/dcmEoC\nJEACJEACJOAAAqdOndKR8bHue/HixZIsWTIHtCq6m3D06FEdQR9bEc6YMSO6YbD3JEACJOAgAlxz\n76CHwaaQAAmQAAmQAAncI3D9+nWpV6+epEuXTiuRVOzvsUnIo4ceekhmzZolc+fOlY8++ighm8K6\nSYAESIAEXAhQuXeBwUMSIAESIAESIAHnEGjbtq0cOXJEFi1aJOnTp3dOw9gSqV69ugwdOlR69eql\nnw+RkAAJkAAJJDyB5AnfBLaABEiABEiABEiABGISGDBggMyePVuWL18uBQsWjHmRZ44g0KlTJ9mz\nZ4/ennDLli1StGhRR7SLjSABEiCBaCXANffR+uTZbxIgARIgARJwKIEFCxYItl4bPny43nrNoc1k\nsxSBW7duSbVq1eTcuXOybds2yZgxI7mQAAmQAAkkEAEq9wkEntWSAAmQAAmQAAnEJrB3716pXLmy\n3vZu1KhRsTMwxXEEzp49K2XKlJFixYrJ0qVLGfTQcU+IDSIBEogWAlTuo+VJs58kQAIkQAIk4HAC\n58+f15HxCxQoICtXrtTbrTm8yWze/wjs3LlT/vGPf0j79u25XSHfChIgARJIIAIMqJdA4FktCZAA\nCZAACZDAPQJw727UqJG2+s6ZM4eK/T00EXFUunRpGTdunA6yN3ny5IhoMxtJAiRAAomNAAPqJbYn\nyv6QAAmQAAmQQAQS6NChg+zevVs2b94smTNnjsAesMkvvfSSDrD36quvSuHChaV8+fKEQgIkQAIk\nEEYCdMsPI2xWRQIkQAIkQALRTODixYtuA64NGzZMOnfuLAikV6dOnWhGFPF9v3v3rtSuXVsr+du3\nb5dcuXLF6NOVK1ckadKkkjp16hjpPCEBEiABEog/Abrlx58hSyABEiABEiABEvBBYP/+/doi37Vr\nV4ECaArW1nfp0kU++ugjKvYmlAj+huL+1VdfSdq0afWOBzdv3rR6s2PHDr2tYcuWLa00HpAACZAA\nCQSPAJX74LFkSSRAAiRAAiRAAh4I/N///Z+22A4ZMkRq1aolsOAeOnRImjRpIs2aNZNu3bp5uJPJ\nkUYgffr0snDhQjl48KDARR8ybdo0qVSpkiBo4qJFiwReHBQSIAESIIHgEqBbfnB5sjQSIAESIAES\nIAEbgTt37kj27NnlwoUL+kry5MklX758YqavX79eUqVKZbuLp5FOYNmyZXoip2rVqrJ27VqrO8mS\nJZMvv/xSXnvtNSuNByRAAiRAAvEnQMt9/BmyBBIgARIgARIgAS8EVq1aZSn2yHb79m355Zdf5PTp\n0wI3fSr2XuBF8CVY6osUKSKYvHEVLMuYMGGCaxKPSYAESIAEgkCAyn0QILIIEiABEiABEiABzwTg\nkg9rvatAwf/777+lcePGMmrUKNdLPE4EBLDkomTJkvLzzz/HiLGArhmGIdu2bZOjR48mgp6yCyRA\nAiTgHAJU7p3zLNgSEiABEiABEkh0BC5fvizz58/X1np752DBxad9+/byxhtvuM1jv4fnzicAd3zs\ne3/y5EmPzxSTPViHTyEBEiABEggeASr3wWPJkkiABEiABEiABGwEZs2a5VHBc806YsQIWbp0qWsS\njyOUwDvvvCNXr171+tzhuTF+/PgI7SGbTQIkQALOJEDl3pnPha0iARIgARIggURBwJcCBwsuoqsj\nH/e4TxSPXNatW6d3QEBvEDzPkyDuwtatWz1dZjoJkAAJkECABKjcBwiM2UmABEiABEiABPwjcOzY\nMdmyZUusNde4G0pfkiRJpG3btoJ8bdq00ef+lcxcTiaAnRGmT58ua9as0bsieFLwU6RIIVOnTnVy\nV9g2EiABEogoAlTuI+pxsbEkQAIkQAIkEDkEJk+eHCuQHloPpb5EiRI6qBqC6WXMmDFyOsWW+k2g\nWrVqcuDAAenbt6+kTJky1ruAgIpTpkzRgRX9LpQZSYAESIAEPBKgcu8RDS+QAAmQAAmQAAnEhwBc\n7bG22hS44KdNm1ZGjhwpO3fulDJlypiX+J1ICUCp79mzpxw8eFCqV6+ue4nJHVMuXbokK1asME/5\nTQIkQAIkEA8CVO7jAY+3kgAJkAAJkAAJuCewYcMGHS0dV0237BYtWujtz1577TVJmpR/grgnlzhT\nCxQooJX4uXPnSrZs2SwrPiZ8sFUihQRIgARIIP4E+D9r/BmyBBIgARIgARIgARuBSZMm6RQo8UWK\nFJFNmzbJxIkTJUuWLLacPI0mAg0bNpTDhw/Lm2++qSd44NmxcOFCwZaJFBIgARIggfgRSGIoiV8R\nvJsESIAESIAEAieAtbZ9+vQR/jcUOLtIuOP48eO6mVhPny5dukQZLO++++6TVatWSe7cuUPySBBJ\nHt4Od+7cCUn5CV3orVu35Pz584LvrFmzSpo0aRK6Saw/BARat24tH3zwQQhKZpEkQAJ2AsntCTwn\nARIgARIggXAQ2Ldvn/6jvmPHjuGojnWEmQCeb/78+fUa+zBXHZbqLl68KIMGDZLTp0+HTLmHhRs7\nCQwYMCAsfUqISjC5t3//fh1VH/EYKImLwIwZM2TXrl2Jq1PsDQk4mACVewc/HDaNBEiABBI7gRw5\ncki3bt0SezfZv0RIAJ4JUO5DLYhXwDESasosP1QEMMn3119/hap4lksCJGAjwDX3NiA8JQESIAES\nIAESIAESIAESIAESIIFII0DlPtKeGNtLAiRAAiRAAiRAAiRAAiRAAiRAAjYCVO5tQHhKAiRAAiRA\nAiRAAiRAAiRAAiRAApFGgMp9pD0xtpcESIAESIAESIAESIAESIAESIAEbASo3NuA8JQESIAESIAE\nSIAESIAESIAESIAEIo0AlftIe2JsLwmQAAmQAAmQAAmQAAmQAAmQAAnYCFC5twHhKQmQAAmQAAmQ\nAAmQAAmQAAmQAAlEGgEq95H2xNheEiABEiABEiABEiABEiABEiABErARoHJvA8JTEiABEiABEiAB\nEiABEiABEiABEog0AlTuI+2Jsb0kQAIkQAIkQAIkQAIkQAIkQAIkYCOQ3HbOUxIgARIgARIggQAI\nDB06VO677z7p0KFDAHeJHD16VPr37y/9+vWTBx98MKB7A8l88+ZNWb9+vezevVuefPJJqVChgiRN\n6v/c/pkzZ+TgwYNStWrVWNX++eefMn78eDlx4oTUqlVLatSoIcmSJYuVb9OmTbJy5UpJkSKFPPPM\nM1KuXLkYebZv3y6HDx+OkWaeoL0FChQwT/kdgQQS6xjx9/1fs2aNLF26VHLmzClNmzaV3Llze32K\ne/bskW+//VZSpkypxxV+HzhGvCLjRRIgAZOAQSEBEiABEiCBBCDw7rvvGmXKlEmAmoNbZfHixY3y\n5csHXOjs2bMN9X+xof7oD/hef284e/asoRRjY+zYscb58+cNMFdKuHHnzh2fRZw7d87o3Lmzcf/9\n9xtvvvlmrPwXLlwwHn74YaNly5ZG9erVDTVhYCilPVY+3Js+fXojb968ur9JkiQxBg0aZOW7e/eu\nLgcs3H127txp5XXSwbFjx3R7ldIVsmZNnTrVUApeyMoPV8GJcYz4+/4PHDjQePTRR41XX33VyJEj\nhx4nixcvdoseY7Rt27bG888/b/zyyy9WnkgdI+hA8+bNjXr16ll94QEJkEBoCfg/dW/OBvCbBEiA\nBEiABEjAIrB161ZZu3atde7vwT//+U9Rf8yL+kPe31sCyqcUAmnUqJGUKFFCXnnlFcmSJYt8/PHH\n8sMPP8h7773ns6zjx49Lq1at5Pr1627zzpo1S7Zt2yaTJ0+W1atXS58+ffT5xo0brfzz5s3TXgJK\nERKU980330jGjBmlZ8+e2nMBGZEGq79SlgVeBuYHlv78+fNLqVKlrPJ4EJkEEuMY8ef9h3cO3uF9\n+/bJ6NGj5eeff5a0adPKsGHDYj1IjI+iRYvq9x9WfjUZZuXhGLFQ8IAESMAHASr3PgDxMgmQAAmQ\nAAl4I5A6dWpR1m1vWTxeg8IdKoFb74YNG6Rdu3ZWFXCZf/nll+WLL76Qq1evWunuDsqWLStFihRx\nd0lu3bolzz77rGTKlMm6jokASLp06ay0zZs3y+DBg7WrvrLYa7f9Jk2ayO3bt7WbMTKmSZNGPv30\nU60EwQ3Z/Hz99dd6csIqjAcRSyCxjRF/3/+///5b8L6bgne9QYMGMcYIrqG8F198UY+nUaNGmdmt\nb44RCwUPSIAEfBCgcu8DEC+TAAmQAAlEL4G//vpLRo4cKT169JAJEyZoq7dyaY8BRLmv62tmIhTX\nVatWaWv2tWvXZObMmXpd/U8//WRm0d+wrMPij7W0oZD58+frYmG5dxXlIqwVe1gH4ypQwO3r4Pfu\n3Su1a9fWngJmuV27do21Bh95ILDgQypWrBgrBgDYwOrfsGFDnYf/OJsAYiogfsSHH34oK1asEHhq\nuIp9jODayZMn5bPPPhM8a3iTDBgwQKZMmaLPzXudOkb8ff8LFy5sdkV/oz9HjhyRd955J0Y6PFnw\nO4DxgokQu3CM2InwnARIwBMBBtTzRIbpJEACJEACUU3g4sWLOvjcuHHjtHu6Wlsuaj2swKJduXJl\nbZGGMqLWlMsDDzwgbdq0EdyDwHozZswQtdZUK/1Zs2bV57DIQYmBtfvHH3+U3r17y5w5c/TkAcp0\nJ7B82ycT7Pny5csnefLksSdrF2AkIoiXq2TLlk2f2icbXPMEcqxWD4qKHyB9+/bVip3rvei7XaDU\nQbFHoDxPAtd+WPqh1FCcTeDzzz/XwRLxLm/ZskVq1qypFVQETYSyv3///hhjBL1ZtGiRHktYloL3\nBxNDOH7//ffl119/1ZNp/o6RU6dOWUs8PJHCu4Qxaxe4yUPiM0a8vf+u9f32229aecc7bW/LV199\nJcmTJ9fu+yp+hV7eguUocN/3tCyFY8SVLo9JgAQsAqFd0s/SSYAESIAESMA9AacH1FPWekMpzlbj\nEdhN/edpKBdyKw0HyrpsZM+e3UpTa9R1vmrVqhnKLVenL1y4UKcppcbKpxQanaY8A6w0+4Fycdd5\nUK+nj7J42m/T50opMJQbfqxrap28LuuNN96Idc2eoNa/67zuAuohr/JsMJTbv6EmN3S+DBkyGCjf\nm4CLUlq8ZTE6depk+NM+r4WE+CID6hnGpUuXDLVThDFx4kSLdp06dQy8B8pKbaXZxwgudO/eXb8z\naj25lQ/vbOnSpa1zf8aIisSvy/E0PpCudmmwynQ9iO8Y8ff9V548hrLiW+1EkDlT1GSGTn/iiScM\nBOmDHDp0yFATDoZyxzdw3Z1EwhhBuxlQz93TYxoJhI4A3fLVrz6FBEiABEiABOwE4D4LayLWw0Ie\nf/xxbZGE5dlVUqVK5Xqqt8WDpVBFktfWOFwsVqyYzoMt40yx32emu35jGzq49nv7wJXXnWCdrjsx\nPQFU5G53lwNKgwvxmDFj5MqVK3rdPL69bQmIdfSwkv773//2WI/6k0fmzp3L9fYeCTnnAqzRN27c\n0NZ2s1WVKlUSbBGHJS2muHvXzTgVrnEdME4CHSNKyfU6PjB21CSE2ZQY3/EdI/6+/08//bTeThJB\nI5USL9OmTZMlS5botuzatUt/169f34ph8cgjjwi2DzSXBcVotDrhGLET4TkJkIBJgMq9SYLfJEAC\nJEACJOBCQFmYtdKAoHQQuNxD0cc+7YGKufc7/igPRKAA+frAndedwFUfijyiz7sKFHCIOeHgei2u\nx2obPHnrrbf0Gvnvv/8+Vp0oFy7QiFuAjzeBuzE4V6lSxVs2XnMAASjmmKzBzgamqO0X9ZILRIUP\nVDBOAh0jeP99jRFzIsHenmCNEX/ef9SdX0XOh2IPwRIGiNomUn/bg2uaS1IOHjyor7v+wzHiSoPH\nJEACrgTc/0XgmoPHJEACJEACJBCFBLB93OHDh6V9+/Y6WBiC32Erueeeey5sNGC9syvn9sqfeuop\ngbXULthWCwJPg4IFC1qXf//9d30cTOXeLBwWSnCyW2phye3Tp4/eNs9+zbzX/MbabbUvdqxAfOZ1\nfjuHADxU1J7tgm0d1TIbUS71esyYCmw4WopAdNgqzptg0sCdh0uwx4in99+1bRh3uXLlEtNzBlZ6\niFr245pNb4WnlhPorfNiXFAnHCN2IjwnARIwCVC5N0nwmwRIgARIgARcCMAiCKskLM2wqtWtWzeW\n0uqSPSSHCxYs8LllnVrv71a5R/A/BDSDlc9VuYcSAddgU6kIZsMRPE2tuY5RJNyioVghMrpppUSG\n06dPa3d+13bAagvFZezYsTHK4IlzCSCY5Ouvv64nZPB8mzZtGtbGIjAk3hlvgrHsTrkP9hhx9/7b\n24WlPpjsQuBBCJR8bCtpWvLN/PB0wVZ69uB7HCMmIX6TAAm4I0Dl3h0VppEACZAACUQ9AWyBB6UB\n1ki4iWMtMP4Qt7sbw7KONb3YAg9KBNbJ4g9wc60+QJrWchVsz+JqWuTNa9YFlwPsVR9XQVs7duwo\nn3zyiY72Dysr1kcjUjmic8OV2BQoPn/88YdgZwBXwVIECO5zFfQDXgWwsGNrPQi2P4NLPso3BcoJ\nrLqYTMAOAqagLvRt2bJlZpL+xu4A4FejRo0Y6TxxJgG841BSu3XrpidqsNUbxkHu3Ln1bgdmq+1j\nBOmXL1/Wl+3jBHkxfvC++jNGsCsFPnGRQMYIdrrA+n5s2VeyZEm/3v/ly5cLtgHEGMAkCGT8+PEy\naNAgKVSokNXkIUOG6KUM2FLQ9MKBBww8C1q3bm3lwwHHSAwcPCEBErARoHJvA8JTEiABEiABEgAB\nWO337dsnWHvvKnC9xRZ4sFJCGV6/fr1WfrFXNSyY2L4KgnXIcFnGVlYfffSRTps6daouDwrQ4MGD\nddrMmTO1slCrVi19Hsx/oNhjwgFeB1DCYC3HdmNok6tAIYfCjTX6ZnwAKN6TJk3S2eBBgO36sEc9\nFCIocQh616tXLylTpoxeqgDvhqVLl4prkLJWrVppBd6uxKNQTCjA7dhVsKUeLP/YR5zifAKYICpQ\noICeRHJtLcYGJn+aNWsWa4x07txZDhw4IPPnz9e3YGzAw2TdunXy3Xff6UmCfv36CcaZOZacMEZg\nlUcbEQAPwTX9ef+xJAZ72mNSAB4NmPSoWrVqrHgSxYsX1x42yAtLPZauQIlfvXq1FZTT5MsxYpLg\nNwmQgDsCSdTsaGDRfdyVwjQSIAESIAESCJAAlDtYp7Bm1omitq8SRAN/8sknxYxaf/XqVW3NL1Gi\nhKitvJzYbLdtgtIODwG48LsTWMthZcf+84EI3IuhiJtWyUDudZcX0cTV9n+SOXNmd5cdlXb8+HGt\n2OL9xQRHKARr19u0aWNZsENRR3zKhGUdk0Vq20LtuQFrPLw6MF6goMO13D6BE5/6QnmvrzGCuqGs\nIwifKf68/5gIgyt+tmzZYngzmGXYv0+dOqUDBHoai5E0RtC3Fi1aaG8cTBBSSIAEQk+AlvvQM2YN\nJEACJEACEUYA69LhDgtXfFiyXdesw5I/a9asiOoR+uBJsUdHXK3tgXRM7WceSHafeWEFpkQOgZYt\nWwqiuudXUeDxcRV4gsBrJFLE1xhBP1wVe5z78/7Du8Hb2EM5roJge96EY8QbHV4jARKInF9dPisS\nIAESIAESCBOBvXv3ahd2uN3DPThfvnwCS+22bdsE13r06BGmlrAaEnAuga1bt+pxAgUf2+JBmcfE\nGNaOFy5c2C9LtXN7x5aRAAmQQOQRuBdNJ/LazhaTAAmQAAmQQEgIwGqPNfEIAof1sLDQwUoJ93W4\nG7tGfQ9JA1goCUQAgSVLlujAcFhPnilTJh0Abvr06TpuQsOGDSOgB2wiCZAACSQuArTcJ67nyd6Q\nAAmQAAkEgQAidSO4FT5Yix4p64aD0HUWQQJ+E8BOCdgqEoKo9wyE6Dc6ZiQBEiCBkBCg5T4kWFko\nCZAACZBAYiFAxT6xPEn2I5QEqNiHki7LJgESIAH/CFC5948Tc5EACZAACZAACZAACZAACZAACZCA\nYwlQuXfso2HDSIAESIAESIAESIAESIAESIAESMA/AlTu/ePEXCRAAiRAAiRAAiRAAiRAAiRAAiTg\nWAJU7h37aNgwEiABEiABEnBPAEH+Vq9eLW+//bYsXbrUfSaHpZ45c0bWrVvns1UXLlyQjz/+2Gc+\nZiABXwROnDghI0eOlFdeecVXVkdc93eMoLGzZs3SW3M6ouFsBAmQgGMIULl3zKNgQ0iABEiABEjA\nPwL79u3Tf9wPGzZMTp065d9NCZTr/Pnz0qVLF3nooYdk/vz5PlsBReyzzz7zmY8ZSMAbAWxbuXHj\nRunfv78sX77cW9YEvxboGNmxY4e0aNFCdu3aleBtZwNIgAScRYDKvbOeB1tDAiRAAiRAAj4JlCpV\nSt544w2f+ZyQ4fjx49KqVSu5fv26z+aMHTtW9u/f7zMfM5CALwJp0qSRZs2aSfny5X1lTfDrgYyR\nq1evSp8+ffQWnQnecDaABEjAcQSo3DvukbBBJEACJEACJOCbQPLkyXWmJEmS+M6cgDnKli0rRYoU\n8dmCn376Sb7//nupXbu2z7zMQAL+EsA4SSxjBH3u0aOH9OzZ09/uMx8JkECUEfjvXwZR1ml2lwRI\ngARIgAT8IWAYhqxfv152794tyZIl00rqM888Y90KhXTLli2yd+9eqVy5sjRo0MC6hoMDBw4I1tE+\n9dRTsmzZMjl06JA0btxY8uTJI3fv3tVuw5s3b5YqVapIhQoVrHt//fVXWbhwobRv317Xv2LFCsmd\nO7e0bdtW7r//fiufp4NvvvlGtm7dKhkzZpQmTZpI5syZraznzp2TJUuWCL4ffvhhgRcAXOYTUhBD\n4P3335fx48dL7969E7IprDtAAr7GCDw2EGsBLuQYQy1bttTvslmNE8eIrz6ZbQ/3N5a1PPLII1K8\nePFwV836SIAEIoQAlfsIeVBsJgmQAAmQQPgJQOEsUKCAvPXWW4J1rnCFN5V7rHf/+uuvZc2aNfLL\nL79ItWrVtCIPhfzKlSvSt29fGTJkiDRs2FDmzJkj6dOnlw0bNkjXrl214j516lTJlSuXzJw5U1vi\ncA0uxNOmTZNOnTrJjRs3BGvrb926pcsdOHCgTJkyRZeRIkUKtzCQF22sUaOGtoBjvTGUZUxQFCtW\nTP7880954YUXtLKFSQIoWhBPyj0mHu7cueO2LjMxX758erLCPI/Ld79+/TTjtGnTxuV23pOABLyN\nEax7h9cG3vXu3bvrQImYBINCf/v2bUeOEaD01ic7asS8OHr0qD05xjk8B9Dv+AjqmTdvnv4NuHz5\ncnyK4r0kQAKJmYCanaSQAAmQAAmQQNgJvPvuu0aZMmXCXq+/FSrLupElSxZj7dq11i1KWbaOCxYs\naChF2jqvX7++oRRn6xwHSqE3lFu6ce3aNZ2u/ig3lGJuKCXeSlNraI2UKVMarmWrYFmGUgiMH374\nwSqvV69ehvp7xBg1apROU2vT9fm4ceOsPIMHDzaUMm+dnzx5Uud59tlnddrnn39uKC8C67pSSozp\n06db5/aDdOnS6ftRr6fPgAED7LfFOr9586a+/80334x1TVl1DbWG2EpXOwAY2bNnt86denDs2DHd\np+3bt4esiUop1u9GyCqIZ8G+xgjanzRpUkN5r+ialAeMZrZt2zar5lCOEVSiPGWMBx980KrP1xjx\n1SeroP8dDB061OPYMMcMxrwv8TZG0CYVP8DieOnSJV2n2gnAV7EJfr158+ZGvXr1ErwdbAAJRAsB\nrrlXv7wUEiABEiABErATgLWtcOHC2q0dFnoIor6bAldjWMYhP/74oyhFWn7++Wfzsv5WyrF2fTdd\n6WGZhrW+UKFClnv9Aw88oC3fSlm07k2dOrVgrbCr+y0sn0j79ttvrXz2A6Vo6HXrsN7jgy3l0Ic/\n/vhDZ4UVFVZ8RNpGhG54JcCzwJNgSYGamPD6gSdCXAWeBF988QXXEMcVYALf52uMIKCdmqASNVmj\nPVHw7kFcx4nTxoivPtmRw8vG1xhRyrj9toDOP/30Ux0cEBwpJEACJOCNAN3yvdHhNRIgARIggagm\nAMUTa+SVVV67usNl3vwDG2vgV65cKYvqL305AAAV5UlEQVQXL9Zr6rF+fefOnT55pUqVKlYeuNkj\nCrY3wSSAskBqpdxdPijKcN3FVnJ16tRxl0WqV6+uJyiwXABr+rHl3L/+9S+3eZFoTkp4zBDPC8pK\nLwi4h7aYAsUPSxLggpwhQwbdZvMav51HwNsYUVZ7PV4++OADue+++/SzRg+UJdprRxJyjKBh3vpk\nbzgm3PAJlSCuB5b1YGIRYwKCyQQIAlAirWLFipIzZ06dxn9IgASim0Dofo2imyt7TwIkQAIkkAgI\nPPHEEzoQGKzmo0eP1sHnsA4+U6ZMotzkrWB3UILnzp3rV49hGXQnntLNvMptV6+9Vy72ZlKMbyhS\nELTPk3KPPJ988onUrFlTOnbsKG3atNGB9bp16xajLPMEngCo15sgWGClSpW8ZfF4Dd4Dq1atinEd\nVk4oL8qFX3suYEKC4lwC3sYIvFGqVq0qX375pY4BAUXVH/E0Fjylm2UGY4ygLG99Musyv9WyDEEA\nS2+CQIJx9XBBcM0TJ07o8WDWodyL9eGsWbN0cEwEoqRyb9LhNwlENwEq99H9/Nl7EiABEiABDwSg\nKOCPZwSdg3JSt25def7557WlDAHr4JIPhd+0bvuyRnqoxu9kBLeDRdvTVnFwb4abvVqHK7CIm+1C\nBQhohoj8UKRhqUdQQFj90Ce1Dl88KfcLFizw6VEAT4a4KvfwerALlKDJkycLlBqKswl4GyPwIOnT\n57/7sZvvbCSMEbzPnsY9+mQX07JuT3c9h2U/rso9JrfsYwGTX1i6g2U3r7/+umtVPCYBEohyAlTu\no/wFYPdJgARIgATcE4B1TAWv0+vTYTGEtVsF2NMfRAGHzJgxQ5o2bSp79uzRa+Gh7OAa7k2TJo1W\njJHmKrhuroE30+GSD8XdVRBNHFHFixYtqpPhGQAruakomet4zbYgkwpSKB06dNCu7PjDHxH6oaBn\ny5ZN8ubNq9c6Q8GH9R9u/lhuoALyuVYb49jb+v4YGX2cXLx4Ueew99HHbbzscALexgiajvf69OnT\nsnTpUilXrpyMGDFC9wjLR7CMBO8n8oRqjKAyjBPUgbZiHPsaI3hHPY173XjbPypgnOATX+EYiS9B\n3k8CJAACVO75HpAACZAACZCABwJwK37ppZekUaNGcvz4cb3vPBRiCFzaYWEuXbq0Xg8LCzjyqsjQ\nMmHCBL2eHUo8trjDdne1atXSLvG//fabYCsrrOvFvvXDhw/XwfiwfR7Ka9WqlS4fLvRQhmCBR7A+\nKCiLFi3S11S0cb2NGE4mTZqk976GVwGseMgL13tszQeLIdbqYns+CNYyY1s/BNvLnDmzVvYnTpyo\nr4Xqn2XLluk2onxMNGCNPSYocuTIEaoqWW4YCXgbI507d9ZbSCJoI7ZgRIyHTZs2CbZ1RHDJCxcu\n6ImuUIwRU0n/7rvv5Pr169qLAO+9rzECdN76FAq0HCOhoMoySSA6CSRRM5n/XbgTnf1nr0mABEiA\nBBKIANxU1TZzgjWrThVYz+FKjKjxsHzbBQq5697ssEC6CwZmv8/XORQQTBBg33oo67Bwwu3eX4Ey\ng7234aYPC70p6A8U/nPnzul2olxK3Ahgsgd88f6qLR3jVoiPuxDAEZNIdsu2j9vCetnXGMH4wfsI\nN3II/uz8+++/RW3/GK92hmqMoFG++hSvhkfZzdiZA95FmNijkAAJhJ4ALfehZ8waSIAESIAEIpSA\nGQXbnWKPLrkq9jgPhmKPclwlT548rqd+HcPa77qNnnmT2R+46VNIIBgEzHfK0xiBB4qp2KM+uMbH\nV7G3tzuYYwRl++qTvX6ekwAJkIBTCPw3tK5TWsN2kAAJkAAJkAAJ6GjxsB66rqcnFhIggXsEEFSO\nY+QeDx6RAAmQAAhQued7QAIkQAIkQAIOIgBX7JUrV2r3ZUSx3717t4Nax6aQQMIT4BhJ+GfAFpAA\nCTiTAN3ynflc2CoSIAESIIEoJYBgcwi+Z0ooXP3NsvlNApFIgGMkEp8a20wCJBAOAlTuw0GZdZAA\nCZAACZCAnwQY5M5PUMwWtQQ4RqL20bPjJEACPgjQLd8HIF4mARIgARIgARIgARIgARIgARIgAacT\noHLv9CfE9pEACZAACZAACZAACZAACZAACZCADwJU7n0A4mUSIAESIAESIAESIAESIAESIAEScDoB\nrrl3+hNi+0iABEiABIJG4MSJE7JkyRLZuXOnjBs3LmjlhqKg48ePy+bNm62iH3nkESldurR17s/B\nsWPHZPny5YJ971944QUJxf72Z86ckYMHD0rVqlVjNenPP/+U8ePHC7gjSGCNGjUkWbJksfIFO8Fb\nm1zrOnr0qGzdutVKKlKkiJQsWdI6j8aDv//+W7799ltZvHixPPPMM/q9cTKHRYsWxdgyslGjRpIy\nZUq/m3zz5k1Zv3693pXiySeflAoVKkjSpMG3fX399dfy7LPPyn333ee2bf6+s25vDiAx0DHJMRIA\nXGYlAQcQCP6vlwM6xSaQAAmQAAmQgJ0A9ozfuHGj9O/fXyu89utOO0dbX3rpJUmSJIlUq1ZNChUq\nFFATBw0aJG3atNEKdcGCBbXy/d133wVUhrfM58+fly5dushDDz0k8+fPj5X1jz/+kDJlysiePXvk\nhx9+kOeff14qVaoUK18wE3y1yV4XJjvQpjx58sjLL78sU6ZMsWeJuvN9+/bJrFmzZNiwYXLq1CnH\n9/+dd96RUaNGSfny5fU4SZEihd9tPnfunBQtWlRPPmGsLFiwQOrWrSt37971uwxfGTGZiHFQv359\nuX79eqzsgb6zsQoIICEuY5JjJADAzEoCDiBA5d4BD4FNIAESIAESCD2BNGnSSLNmzbQSEPraglcD\nlOIcOXJIunTp/C4U1vr33ntPhg4dKrD4wyIJJahBgwby66+/+l2Ot4zwLGjVqpVbhQX3QUHctm2b\nTJ48WVavXi19+vTR55i0CJX4apO9XrwT+fLl03xy585tvxyV56VKlZI33ngjovqONmOSCeMEk2H+\nCBR4WPlLlCghr7zyimTJkkU+/vhjPRGFsRMMgccKyscY9CSBvrOeyvEnPS5jkmPEH7LMQwLOIUDl\n3jnPgi0hARIgARIIA4HkyZP7rQCEoTkhqWLgwIHavdzVxbxFixbafRlu8sGQsmXLCtzY3cmtW7e0\nC3KmTJmsy5gIgAQySWHd7OeBtzb5WQSzKQIYIxB/FWWdOcL+wdKDDRs2SLt27ayWY8kIPDi++OIL\nuXr1qpUe14O8efMKPvnz5/dYRLje2YQakx47zgskQAIhIcA19yHBykJJgARIgASCRQDu9GPHjhX8\ncYq1sLBkP/roo3L58mWZNGmSXLt2TRo2bGi5rf/000+yZcsW2bt3r1SuXFlbqz215fTp0zJv3jzB\nOmOsLy5evLisXbtWu5LjHpSLP85NgZsyrOKwfqNsrCF3mvz+++8C93tTmTbbh7W+Dz/8sLao9+7d\n20wOyTfWPBcoUCBG2XgetWvX1pbMGBd4EhQCgYwTuIevW7dOdu3apWMgtGzZUrx5LmBd+5EjRwRW\nXFi5r1y5oj0yMG5y5swpTZo0idGHb775RscyyJgxo76WOXPmGNedcGIuJYFl3VXw2wLFfunSpdK4\ncWPXSxF9zDEZ0Y+PjScBvwlQufcbFTOSAAmQAAkkBAEoFHArr1ixojz99NPy7rvv6mbAAow/WA8d\nOmQp9lgnjMBVa9askV9++UWvwUWgqvbt27ttOhQTrCl98cUXdYA9KPdY3w7lGApwsWLFLOUeSv9X\nX32ly0qbNq1eQwsF+ssvv3RbNiYCEIzKm8AyikmCYArqhMsx+mYX9HXTpk1iGEbYrLKoa/bs2dK3\nb19ZsWKFvUk8DxIBf8cJJgHgcTF16lTp3r27dkXHO3jgwAEdeNFdc+rUqaMn1C5duqSVe7z/ePcf\nfPBBPSFmKveYgINLPya9MJGD+BYYRwhYh7HkThA08s6dO+4uWWlYOoG4CMGUn3/+WRdnHycYIxBM\nEiZW4ZhMrE+W/SIB5XlFCCRAAiRAAiTgdAJwXYVbOZREKBjp06fXTd6xY4e8//77VvOhaCMiNZTm\n/MoV9oknntBRvz0p97jRndLh6s6OPFCIYLGE9Tl16tTa5R2K6ogRIwRWT0TYtsvMmTP1Ond7uus5\ngn9BIQqmnD17VheHCPl2eeCBB3R9Fy5c0GuM7deDfQ4L6Ntvvy3Tpk3THhawkq5cuVLwPCnBJ+DP\nOMHkFzxWEEgObuhQ3Hv16qXXmnt7LsgPjxhToOAjUKOrfP7559oDoGnTpjr5008/1Uo54j3A48Wd\nPPfcc9oLx901M23AgAE6hoR5HoxvjBP03x5ZH2MEAkaJUTgmE+NTZZ9I4B4Brrm/x4JHJEACJEAC\nDiYAiyBc8GFxhMA1GB9Y9UyBqzGshZAff/xRTp48KaaFzswTl29Y7OHK3LVrV22ZRFvgEQA398OH\nD7stslOnTrq9aLOnDyYqgi2w4ELcrZeGhTRVqlQCd+lwCCZCxowZo58TFD08rw4dOoSj6qitw9c4\nQVBJ7F6QPXt2uXHjhraqA1YwxgkCOH7//ffWGEGAusKFCwuitHsSjCNP48NMx7gLtpjjxF6u6UWA\n4HyJUTgmE+NTZZ9I4B4BWu7vseARCZAACZCAgwnAqojP6NGjtfIwY8YMad68eYwWY90wLMPYo/up\np57Syjf2tI+v7N+/X7u5e3LBd1c+gpKZgcncXQ9Vmum+7C4gGJRrRO4Ox17zrv1DrIS33npLLwlA\njAPsLY5JBkrwCfgaJ3gWUOw/+OADvee6aa2P7/Zv2D8dS1Hg4QJvAH/FnYeJv/fGJx/GCRR5+7uI\nMQJx59ETn/qcdi/HpNOeCNtDAsEhQOU+OBxZCgmQAAmQQBgIwCrZunVrwTrdZcuWaTd912rhXoz1\nvXCZh9Iwd+5c18txPoYyjLX9CCDm7z7a27dvFwQW8yYoN9hWSSgtsM7Ba8EuCLZnX3JgzxPKc8RM\nQOwCKvahpCx68svTODl27JhUrVpVx4rAuvhgrS2HsgjZt29fQMo9rP1QsL0JJuoqVarkLUvA17DM\nAIJx4rq8AGMEktiVe91J9Q/HpEmC3ySQOAhQuU8cz5G9IAESIIGoIIDAXZ07d9bruLFW19UCDaUF\nLvmw7JvWQH+skaZ1HS7KnuTxxx/XEbRHjRolcLc3BdbK6dOnu3U1h9I0Z84cM6vbb9Q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       "prompt_number": 10,
       "text": [
        "<IPython.core.display.Image at 0x112e38b90>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##What to do first?\n",
      "[Andy Mueller](http://peekaboo-vision.blogspot.com/) (scikit-learn contributor) put together this [cheat sheet](http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png) a few months ago which is extremely helpful."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url=\"http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png\",\n",
      "      width=700)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png\" width=\"700\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Image at 0x1082d7150>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Try building a linear regression model for the Boston Housing Dataset to predict Home Prices"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_boston\n",
      "boston = load_boston()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Prep the data\n",
      "\n",
      "- Create a dataframe with the Boston data\n",
      "- snake_case/lower_case the columns\n",
      "- print to the console"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "\n",
      "\n",
      "def camel_to_snake(column_name):\n",
      "    \"\"\"\n",
      "    converts a string that is camelCase into snake_case\n",
      "    Example:\n",
      "        print camel_to_snake(\"javaLovesCamelCase\")\n",
      "        > java_loves_camel_case\n",
      "    See Also:\n",
      "        http://stackoverflow.com/questions/1175208/elegant-python-function-to-convert-camelcase-to-camel-case\n",
      "    \"\"\"\n",
      "    s1 = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', column_name)\n",
      "    return re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', s1).lower()\n",
      "\n",
      "df = pd.DataFrame(boston.data)\n",
      "df.columns = [camel_to_snake(col) for col in boston.feature_names[:-1]]\n",
      "# add in prices\n",
      "df['price'] = boston.target\n",
      "print len(df)==506\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n",
        "/usr/local/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n",
        "/usr/local/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>crim</th>\n",
        "      <th>zn</th>\n",
        "      <th>indus</th>\n",
        "      <th>chas</th>\n",
        "      <th>nox</th>\n",
        "      <th>rm</th>\n",
        "      <th>age</th>\n",
        "      <th>dis</th>\n",
        "      <th>rad</th>\n",
        "      <th>tax</th>\n",
        "      <th>ptratio</th>\n",
        "      <th>b</th>\n",
        "      <th>lstat</th>\n",
        "      <th>price</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.00632</td>\n",
        "      <td> 18</td>\n",
        "      <td> 2.31</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.538</td>\n",
        "      <td> 6.575</td>\n",
        "      <td> 65.2</td>\n",
        "      <td> 4.0900</td>\n",
        "      <td> 1</td>\n",
        "      <td> 296</td>\n",
        "      <td> 15.3</td>\n",
        "      <td> 396.90</td>\n",
        "      <td> 4.98</td>\n",
        "      <td> 24.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.02731</td>\n",
        "      <td>  0</td>\n",
        "      <td> 7.07</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.469</td>\n",
        "      <td> 6.421</td>\n",
        "      <td> 78.9</td>\n",
        "      <td> 4.9671</td>\n",
        "      <td> 2</td>\n",
        "      <td> 242</td>\n",
        "      <td> 17.8</td>\n",
        "      <td> 396.90</td>\n",
        "      <td> 9.14</td>\n",
        "      <td> 21.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.02729</td>\n",
        "      <td>  0</td>\n",
        "      <td> 7.07</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.469</td>\n",
        "      <td> 7.185</td>\n",
        "      <td> 61.1</td>\n",
        "      <td> 4.9671</td>\n",
        "      <td> 2</td>\n",
        "      <td> 242</td>\n",
        "      <td> 17.8</td>\n",
        "      <td> 392.83</td>\n",
        "      <td> 4.03</td>\n",
        "      <td> 34.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.03237</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2.18</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.458</td>\n",
        "      <td> 6.998</td>\n",
        "      <td> 45.8</td>\n",
        "      <td> 6.0622</td>\n",
        "      <td> 3</td>\n",
        "      <td> 222</td>\n",
        "      <td> 18.7</td>\n",
        "      <td> 394.63</td>\n",
        "      <td> 2.94</td>\n",
        "      <td> 33.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.06905</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2.18</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.458</td>\n",
        "      <td> 7.147</td>\n",
        "      <td> 54.2</td>\n",
        "      <td> 6.0622</td>\n",
        "      <td> 3</td>\n",
        "      <td> 222</td>\n",
        "      <td> 18.7</td>\n",
        "      <td> 396.90</td>\n",
        "      <td> 5.33</td>\n",
        "      <td> 36.2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "      crim  zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
        "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
        "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
        "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
        "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
        "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
        "\n",
        "        b  lstat  price  \n",
        "0  396.90   4.98   24.0  \n",
        "1  396.90   9.14   21.6  \n",
        "2  392.83   4.03   34.7  \n",
        "3  394.63   2.94   33.4  \n",
        "4  396.90   5.33   36.2  "
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Fit your model\n",
      "\n",
      "- define your features\n",
      "- create a LinearRegression\n",
      "- fit the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression\n",
      "\n",
      "features = ['age', 'lstat', 'tax']\n",
      "lm = LinearRegression()\n",
      "lm.fit(df[features], df.price)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Plot the predicted values against the actual values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add your actual vs. predicted points\n",
      "pl.scatter(df.price, lm.predict(df[features]))\n",
      "# add the line of perfect fit\n",
      "straight_line = np.arange(0, 60)\n",
      "pl.plot(straight_line, straight_line)\n",
      "pl.title(\"Fitted Values\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.text.Text at 0x112ee6050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CsVTunJO69W2kigF/WZ8QAghUIjCNwO9UC8SjPQR3B5V76Q8CNfjAAw+TJN95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\nampqxGazyaJFS2TkyAkyefIfpLy8XGw2m2RnjxK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PPffcQ3x8PFlZWbz55puq60ZRFMWF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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112ef5750>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Redo the model, but this time have a training/test set."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##We just did the following\n",
      "\n",
      "- Learned about `scikit-learn` and what it's used for\n",
      "- Used CART to build a decision tree classifier and visualized it\n",
      "- Built a regression model using `scikit-learn`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}